Pre-fetching of information to facilitate channel switching

ABSTRACT

Aspects of the subject disclosure may include, for example, receiving, for a selected channel, a first video; processing the first video for rendering on a display being viewed by a user; selecting from among a plurality of channels a subset of channels for which to pre-fetch data, the selecting being according to predictions that each channel of the subset of channels is more likely to be requested by the user than each channel of the plurality of channels that is not part of the subset; prioritizing the subset of channels such that a first channel of the subset of channels has a priority over a second channel of the subset of channels, the first channel being given the priority based upon a prediction that the first channel is more likely to be requested by the user than the second channel; pre-fetching, for the first channel, first data of a first type and second data of a second type; and pre-fetching, for the second channel, third data of the first type without pre-fetching any data of the second type. Other embodiments are disclosed.

CROSS REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.16/586,520, filed on Sep. 27, 2019. All sections of the aforementionedapplication(s) and/or patent(s) are incorporated herein by reference intheir entirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to pre-fetching of information tofacilitate channel switching.

BACKGROUND

A conventional streaming media approach is to implement a streamingmedia player as a single component which both accesses network resourcesand renders the media. In such a case, a switching between two mediastreams can require shutting down the current media player,instantiating a new one, and acquiring the new stream metadata, new DRMlicense and new media. In addition, this can involve shutting down andreestablishing rendering mechanisms on a device. Further, variousconventional processes may take a significant amount of time.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an example, non-limitingembodiment of a communication network in accordance with various aspectsdescribed herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 in accordance with various aspects described herein.

FIG. 2B depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2C depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2D depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for pre-fetching of information to facilitate channelswitching. Other embodiments are described in the subject disclosure.

As described herein, one or more embodiments provide for determining (orpredicting): (a) for which channels to pre-fetch information; and/or (b)when the information should be pre-fetched for one or more channels.

As described herein, one or more embodiments provide a streaming mediaplayer which uses different software components and/or differentfirmware components and/or different hardware components to separatefunctions for fetching the media content (e.g., video), for renderingthe fetched media content (e.g., video) and/or for pre-fetchinginformation associated with a plurality of channels that are predictedto be desired (or requested) by a viewer. In one specific example, theinformation that is pre-fetched is initialization data, manifest dataand/or digital rights management (DRM) data.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communication network 100 inaccordance with various aspects described herein. For example,communication network 100 can facilitate in whole or in part determining(or predicting): (a) for which channels to pre-fetch information; and/or(b) when the information should be pre-fetched for one or more channels.Further communication network 100 can facilitate in whole or in partpre-fetching of such information. In particular, a communicationsnetwork 125 is presented for providing broadband access 110 to aplurality of data terminals 114 via access terminal 112, wireless access120 to a plurality of mobile devices 124 and vehicle 126 via basestation or access point 122, voice access 130 to a plurality oftelephony devices 134, via switching device 132 and/or media access 140to a plurality of audio/video display devices 144 via media terminal142. In addition, communication network 125 is coupled to one or morecontent sources 175 of audio, video, graphics, text and/or other media.While broadband access 110, wireless access 120, voice access 130 andmedia access 140 are shown separately, one or more of these forms ofaccess can be combined to provide multiple access services to a singleclient device (e.g., mobile devices 124 can receive media content viamedia terminal 142, data terminal 114 can be provided voice access viaswitching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system 200 functioning within the communication networkof FIG. 1 in accordance with various aspects described herein.

As seen in this FIG. 2A, system 200 includes renderer 202, fetcher 204,display 210, speaker 212, pre-fetcher 206 and pre-fetcher 208. Inoperation, fetcher 204 obtains media content (e.g., including videocontent and audio content) from the Internet (see communication path“A”). The media content can be obtained from one or more servers. Themedia content is sent from fetcher 204 to renderer 202 (seecommunication path “B”). Renderer 202 renders the media content anddelivers the rendered media content to the display 210 via communicationpath “C” and to the speaker 212 via communication path “D”. In addition,fetcher 204 obtains from the Internet (and then sends to renderer 202)any required ancillary information such as, for example, streammetadata, initialization data, manifest data and/or digital rightsmanagement (DRM) data (e.g., one or more licenses, one or morepermissions). In one example, the ancillary information that is sent tothe renderer 202 can be a sub-set of information that is obtained by thefetcher 204 from the Internet. In one specific example, information thatis obtained by the fetcher 204 from the Internet (and that is not inturn sent by the fetcher 204 to the renderer 202) can be used by thefetcher 204 to calculate what to fetch next). In another example, theancillary information can be obtained from one or more servers. Inanother example, the fetcher 204 can obtain the ancillary informationfrom a network other than (or in combination with) the Internet.

Still referring to FIG. 2A, pre-fetcher 206 operates (e.g.,independently of fetcher 204, independently of pre-fetcher 208 andindependently of renderer 202) to obtain information from the Internet(see communication path “E”). The information can be obtained from oneor more servers. The information obtained by pre-fetcher 206 can includeinformation such as, for example, stream metadata, initialization data,manifest data and/or DRM data (e.g., one or more licenses, one or morepermissions). The information obtained by pre-fetcher 206 can be used tofacilitate a quick channel change to another channel (that is, a channelthat is different from the one currently being rendered by renderer202). The information obtained by pre-fetcher 206 can be sent (via acommunication path not shown in this figure) to the renderer 202 upon arequest by a user for the channel change to the channel associated withthe information being obtained by the pre-fetcher 206. The informationobtained by the pre-fetcher 206 can be associated with a channel that isdetermined (or predicted) as described herein. In another example, thepre-fetcher 206 can obtain information from a network other than (or incombination with) the Internet. After the channel change, thepre-fetcher 206 can act essentially as a fetcher. In one example, afterthe channel change (wherein the pre-fetcher 206 can then essentially actas a fetcher) the fetcher 204 can then act essentially as a pre-fetcher.

Still referring to FIG. 2A, pre-fetcher 208 operates (e.g.,independently of fetcher 204, independently of pre-fetcher 206 andindependently of renderer 202) to obtain information from the Internet(see communication path “F”). The information can be obtained from oneor more servers. The information obtained by pre-fetcher 208 can includeinformation such as, for example, stream metadata, initialization data,manifest data and/or DRM data (e.g., one or more licenses, one or morepermissions). The information obtained by pre-fetcher 208 can be used tofacilitate a quick channel change to yet another channel (that is, achannel that is different from both the one currently being rendered byrenderer 202 and the one associated with the pre-fetching by pre-fetcher206). The information obtained by pre-fetcher 208 can be sent (via acommunication path not shown in this figure) to the renderer 202 upon arequest by a user for the channel change to the channel associated withthe information being obtained by the pre-fetcher 208. The informationobtained by the pre-fetcher 208 can be associated with a channel that isdetermined (or predicted) as described herein. In another example, thepre-fetcher 208 can obtain information from a network other than (or incombination with) the Internet. After the channel change, thepre-fetcher 208 can act essentially as a fetcher. In one example, afterthe channel change (wherein the pre-fetcher 208 can then essentially actas a fetcher) the fetcher 204 can then act essentially as a pre-fetcher.

In various embodiments, certain communications between various elementsof shown in FIG. 2A can be bi-directional (e.g., request/response).

As described herein, one or more embodiments provide for splitting amedia player into a plurality of “pre-fetchers”, a single “fetcher” anda single “renderer”. In one example, a fetcher is a component which isconnected to a network and which acquires stream metadata, DRMlicense/permission and media for a specific media stream. In anotherexample, a pre-fetcher is a component which is connected to a networkand acquires stream metadata and DRM license/permission (but, in thisexample, not media content) for a specific media stream. In one example,a given pre-fetcher component can be instantiated before intendedplay/playback of a particular stream and remain active afterplay/playback of the particular stream has ended (wherein the givenpre-fetcher component is ready to be used again). In one example, thereis a single pre-fetcher or fetcher for a given media asset. In oneexample, a renderer is a component which decrypts, decodes and displaysthe stream delivered and prepared by a given fetcher. In one example, asingle renderer is used for rendering media across stream switches (thatis, in this example, a single renderer is used for rendering media froma plurality of fetchers (one fetcher at a time)).

As described herein, in one or more embodiments a streaming media playerarchitecture can enable considerably faster switching between mediastreams. In one example, the switches can be independent switches—suchas between two different live channels, between two different video ondemand (VOD) channels, or between a VOD channel and a live channel. Inanother example, the switches can be between main and secondary content(e.g. between a movie and ads).

In one example, all pre-fetchers are part of a first application, thefetcher is part of a different (second) application and the renderer ispart of a still different (third) application. In another example, eachpre-fetcher, the fetcher and the renderer are part of respectivedifferent applications. In another example, all pre-fetchers/fetcher aremodules of an application and the renderer is a different module of thesame application.

As described herein, each pre-fetcher can be operated in advance(without connection to a renderer) to pre-fetch information that will(or may be) needed in the future. The information that will (or may be)needed in the future can be determined via one or more predictions(e.g., based on “next” or “previous” channels in a list or as otherwisedescribed herein).

In various examples, when switching back and forth between streams(channels) the fetcher(s)/pre-fetcher(s) do not need to re-collectinformation.

In various examples, when switching back and forth between streams(channels) the fetcher(s)/pre-fetcher(s) do not need to be torn down andre-instantiated.

In various examples, when switching back and forth between streams(channels) the renderer does not need to be torn down andre-instantiated.

In various examples, the fetcher/pre-fetchers can download media contentand/or other information (e.g., metadata). In one specific example, apre-fetcher can download metadata (and/or other information) of aparticular stream while the particular stream is not being played (thatis, in this example, download the metadata (and/or other information)without downloading the media content). In one example, the metadata(and/or other information), which can be periodically changing at thesource server, can be periodically downloaded so that the downloadedmetadata is always up to date. In one specific example, the metadata(and/or other information) can be downloaded only at a time that thecorresponding media content is not being downloaded.

In one specific example, the renderer is instantiated once (during agiven session) and is never torn down (removed from memory) during thegiven session (that is, in this example, the renderer does not gothrough instantiation-uninstantiation cycles”).

In one specific example, each pre-fetcher keeps a state of a mediastream up to date, so the information is ready when needed (such as aresult of a channel change).

In one specific example, a single fetcher/renderer combination can be“active” (e.g., obtaining ancillary information as well as mediacontent, such as segments of a media stream) while a plurality ofpre-fetchers are passive (e.g., obtaining ancillary information but notobtaining media content).

In one specific example, each pre-fetcher can periodically (orconstantly) obtain ancillary information that periodically (orconstantly) changes for a live channel.

In one specific example, each pre-fetcher can obtain ancillaryinformation from one or more servers. In one example, the ancillaryinformation can include Media Presentation Description (MPD) data. SuchMPD data can include a list of segments (each segment may be, forexample, 6 seconds of media (video, audio).

In one specific example, there is one pre-fetcher per each of the xnumber (e.g., 10) of the most used channels. In one specific example,there is one pre-fetcher per each of the x number (e.g., 10) of the mostrecently used channels. In one specific example, there is onepre-fetcher per each of the channels that are predicted (as describedherein).

Referring now to FIG. 2B, various steps of a method 2000 according to anembodiment are shown. As seen in this FIG. 2B, step 2002 comprisesreceiving, for a selected channel, a first video. Next, step 2004comprises processing the first video for rendering on a display beingviewed by a user. Next, step 2006 comprises selecting from among aplurality of channels a subset of channels for which to pre-fetch data,the selecting being according to predictions that each channel of thesubset of channels is more likely to be requested by the user than eachchannel of the plurality of channels that is not part of the subset.Next, step 2008 comprises prioritizing the subset of channels such thata first channel of the subset of channels has a priority over a secondchannel of the subset of channels, the first channel being given thepriority based upon a prediction that the first channel is more likelyto be requested by the user than the second channel. Next, step 2010comprises pre-fetching, for the first channel, first data of a firsttype and second data of a second type. Next, step 2012 comprisespre-fetching, for the second channel, third data of the first typewithout pre-fetching any data of the second type.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2B, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 2C, various steps of a method 2100 according to anembodiment are shown. As seen in this FIG. 2C, step 2102 comprisesobtaining, for a selected channel being viewed by a user on a display, afirst video. Next, step 2104 comprises processing the first video forrendering on the display. Next, step 2106 comprises identifying fromamong a plurality of channels a subset of channels for which topre-fetch data, the identifying being according to predictions that eachchannel of the subset of channels is more likely to be requested by theuser, during a subsequent channel switching process, than each channelof the plurality of channels that is not part of the subset. Next, step2108 comprises assigning priorities to each channel of the subset ofchannels such that a first channel of the subset of channels has a firstpriority that is above a second priority of a second channel of thesubset of channels, the first channel being assigned the first priorityand the second channel being assigned the second priority based upon aprediction that the first channel is more likely to be requested by theuser during the subsequent channel switching process than the secondchannel. Next, step 2110 comprises pre-fetching, for the first channel,first data, the first data enabling switching to the first channel in afirst amount of time. Next, step 2112 comprises pre-fetching, for thesecond channel, second data, the second data enabling switching to thesecond channel in a second amount of time, the first amount of timebeing less than the second amount of time.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2C, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 2D, various steps of a method 2200 according to anembodiment are shown. As seen in this FIG. 2D, step 2202 comprisesprocessing, by a processing system including a processor, a receivedvideo for rendering on a display, the received video being associatedwith a channel that has been selected by a viewer. Next, step 2204comprises selecting, by the processing system, from among a plurality ofchannels a subset of channels for which to pre-fetch data, the selectingbeing according to predictions that each channel of the subset ofchannels is more likely to subsequently be selected by the viewer thaneach channel of the plurality of channels that is not part of thesubset. Next, step 2206 comprises assigning, by the processing system,priorities to each channel of the subset of channels such that a firstchannel of the subset of channels has a first priority that is higherthan a second priority of a second channel of the subset of channels,the first channel being assigned the first priority and the secondchannel being assigned the second priority based upon a prediction thatthe first channel is more likely to subsequently be selected by theviewer during a subsequent channel switching process than the secondchannel. Next, step 2208 comprises pre-fetching by the processingsystem, for the first channel, first data of a first type and seconddata of a second type. Next, step 2210 comprises pre-fetching by theprocessing system, for the second channel, third data of the first typewithout pre-fetching any data of the second type.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2D, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon various preferences. These preferences can comprise, forexample: (a) personal preferences (e.g., prior binging); (b) grouppreferences (e.g., trending topics where a certain TV show is beingwatched at a certain time); and/or (c) metadata (e.g., keywords,topics). In various examples, keywords and/or lists can be used in thedeterminations.

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon one or more identities. In one example, the determinationscan be based upon a context of a viewer (e.g., watching alone, watchingwith the viewer's children, viewing venue as public or private, orviewing location within a specific venue). In one specific example ofvenue/location, if a viewer is on a train, then a determination can bemade to pre-fetch information for x number (e.g., 3) or less channels.

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon personalization(s). In one example, the determinations can bebased upon how a viewer is navigating with a given app on a givenservice. In one example, the determinations can be based upon engagementof a viewer (e.g., shallow, wide).

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can beresource-based. In one example, the determinations can be based uponresource(s) specific to a device that is being used by a viewer (e.g.,only pre-fetch content and/or data that will fit on the device that isbeing used by the viewer). In one example, the determinations can bebased upon where the viewer is (and/or where the viewer is predicted tobe) at a given time (e.g., based upon a schedule of the viewer).

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon knowledge that is learned from actions (e.g., viewing habits)of other people who are unrelated to the particular user for which thedeterminations are being made.

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon local attributes (e.g., associated with a device of aparticular user for whom the determinations are being made) and/orglobal attributes (e.g., cloud information, information related to otherviewers, etc.). In one example, the determinations can be based upon amerger and/or ranking of local and global attributes. In one example,the media playing device can provide some or all of the localinformation and the cloud (or the network) can provide some or all ofthe global information.

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon historical data (e.g., associated with the particular userfor whom the determinations are being made and/or associated with one ormore “typical” viewers). In one example, a short term data point (orpoints) can be used to diverge (e.g., based upon current short termviewing habits) the pre-fetch information.

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon a detection of a presence of multiple viewers in a room. Inone example, a given viewer can be identified using, e.g., device MACand/or facial recognition.

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon a detection of a presence of multiple viewers in the samebuilding (e.g., a house). In one example, if it is determined thatmultiple video streams are being sent (and/or have recently been sent)to a house at the same time, then a determination can be made thatmultiple people are in the house (and, possibly, in the same room). Inone example, a determination of users being together in a room can bebased upon detection of a network IP range associated with multiplemedia streams, concurrent use of a given user account, all users beinglogged into FACEBOOK (or other social media platform) at the same time.In one example, a pre-fetching prediction can be based upon a predictionof which user in a given group will “win-out”. In one example, apre-fetching prediction can be based upon users with similar interests.

In one or more embodiments, a group account can be set up. In oneexample, determinations of which information to fetch and/or pre-fetch(e.g., which information for which channels) can be based upon a groupprofile and/or characteristic(s) associated with the group account. Inone example, user input can be provided indicating who the viewing groupis and/or identifying each member of the group. In one example, thepre-fetched information (e.g., for multiple channels) can be split-upbased on the group. In one example, a certain number of channels can bepre-fetched for each member of the group. In one specific example, ifthere are 4 members of a group and 8 pre-fetchers, then for each memberof the group 2 associated channels will be pre-fetched.

In one or more embodiments, mechanisms are provided for different levelsof pre-fetching (e.g., initialization data plus manifest vs.initialization data plus manifest plus digital rights management (DRM)data). In one example, the different levels of pre-fetching can be basedupon different degrees of favorites/prediction.

In various embodiments, manifest data (e.g., which describesinitialization data) can be pre-fetched and then the initialization datacan be obtained (e.g., pre-fetched) based upon such manifest data. Inone example, manifest data and initialization data can be obtained andthe initialization data can be stored persistently (e.g., so that thestored initialization data is available for a given channel such as whenthe application/device is re-started or the user re-tunes to thischannel without leaving the application). Since initialization datatypically does not change often, keeping such initialization datapersistently over long periods of time is possible. As described herein,an example operates in a manner of (in effect) re-using initializationdata that was pre-fetched previously.

In one example, there can be three tiers: (a) “Tier 1” channels havinghighest likelihood of being requested by a viewer during a channelswitching process; (b) “Tier 2” channels having a middle likelihood ofbeing requested by a viewer during a channel switching process; and (c)“Tier 3” channels having lowest likelihood of being requested by aviewer during a channel switching process. In one example, the Tier 1channels can have information pre-fetched which facilities the fastestswitching (as compared to channels of Tiers 2 and 3). In anotherexample, the Tier 2 channels can have information pre-fetched whichfacilities the switching at a speed below Tier 1 and above Tier 3. Inanother example, the Tier 3 channels can have information pre-fetchedwhich facilities the slowest switching (as compared to channels of Tiers1 and 2). In one example, the information that is pre-fetched for theTier 1 channels can be initialization data, manifest data and DRM(digital rights management) data. In another example, the informationthat is pre-fetched for the Tier 2 channels can be only initializationdata and manifest data. In another example, the information that ispre-fetched for the Tier 3 channels can be only initialization data. Inone example, all available initialization data can be pre-fetched and/orthe initialization data can be stored persistently. In one example, allavailable manifest data can be pre-fetched. In one example, the DRM datathat is pre-fetched can be license and/or permission. In one example,the DRM data can be pre-fetched for a fewer number of channels than forthe manifest data and/or than for the initialization data.

In one or more embodiments, content (such as media data) can bepre-fetched (e.g., video on demand (VOD) and/or broadcast).

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon one or more specific features of a device that is being usedby a viewer (e.g., don't pre-fetch a channel where a movie is startingat a mobile device that has limited battery).

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can beimplemented using artificial intelligence (AI). In one example, the AIcan be used in the context of fetching and/or pre-fetching that is basedupon preference, context and/or social identity. In one example, the AIcan provide for one or more decisions that are divergent (e.g., if aviewer seems like they want to watch something different than theyusually watch). In one example, the AI can use feedback. In variousspecific examples, the feedback used by the AI can be based upon: (a) aparticular user (or viewer); (b) other users (or viewers) in the samehousehold, (c) non-related users (or viewers)—e.g., the AI can performstatistical analysis of data associated with a large number of users (orviewers); the statistical analysis can relate to channels among whichsuch large number of users (or viewers) tend to switch). In one example,the AI can determine (or predict) for which channels to pre-fetchinformation and/or when the information should be pre-fetched for one ormore channels.

In one or more embodiments, a switching process to begin rendering videofor a new channel (that is, a channel being switched to) can beperformed within 100's of milliseconds (e.g., 400-500 milliseconds). Inone example, such 100's of milliseconds start time can be based uponpre-fetching of metadata without pre-fetching of video content.

In one or more embodiments, determinations of channels for whichinformation will be fetched and/or pre-fetched can be based upon a listof prior viewed channels.

In one or more embodiments, predictions of which channel(s) will next berequested by a viewer (or desired by a viewer) can be based uponhistory/preferences (e.g., based upon previous visits to variouschannels, the AI can cause pre-fetching of information for 10 channels).In one example, the predictions can be based upon thresholds and/orquantitative metrics (e.g., history of switching to channel X more thanthreshold T). In one example, the predictions can be based upon one ormore factors that are outside of media consumption history (e.g., apurchase history at a sporting goods store increases the likelihood thata sporting channel will be watched). In one example, the predictions canbe based upon other viewers that are detected in the viewing area (e.g.,detected via presence information). In one specific example, thepredictions can be based upon a group of college friends who are likelyto switch to the college football game of their alma mater).

In one or more embodiments, information can be pre-fetched for avariable number of channels. In one example, the number of channels forwhich information is pre-fetched can vary based upon: (a) the particularuser who is viewing; (b) the time of day/day of week (e.g., pre-fetchfor more channels on Saturday afternoon as compared to Tuesday at 2:00am); (c) the type of traffic (e.g., VOD vs. live); or (d) anycombination thereof. In one example, since live traffic is resourceintensive, information for a subset of users can be pre-fetched (ascompared, for example, to VOD). In one example, the number of channelsfor which information is pre-fetched can be set based upon hardwarelimits/capabilities.

In one specific example, the channels for which information ispre-fetched can be carrying sports games, wherein the viewer is likelyto desire to jump between such games. In one specific example, thechannels for which information is pre-fetched can be the channelscarrying the x number (e.g., 5) college basketball games being carriedon a Saturday afternoon.

In one specific example, the channels for which information ispre-fetched can be the last x number (e.g., 10) channels visited. In oneexample, the channels for which information is pre-fetched do not needto be adjacent. In one example, the channels for which information ispre-fetched can be x number (e.g., 5-6) preset channels. In one example,the channels for which information is pre-fetched can be based uponhistory (e.g., profile of customer, based on customer viewing history).In one example, the channels for which information is pre-fetched can bepredicted by AI and can comprise x number (e.g., 10) most likelychannels that a viewer will want to watch on a given day (e.g.,“today”).

In one example, the channels for which information is pre-fetched can bedetermined upon startup, wherein the previous favorites are used and/orthe previous most viewed x number (e.g., 5-6) of preset channels areused.

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon: (a) user preselection of favorites (e.g., based on timeperiods); (b) system preselection; or (c) a combination of both.

In one or more embodiments, determinations of which information to fetchand/or pre-fetch (e.g., which information for which channels) can bebased upon previously viewed channels. In one example, thedeterminations can indicate which channel(s) and which respectivetime(s).

As described herein, various embodiments can provide for fetching(and/or pre-fetching) of content and/or other information (e.g.,metadata). In one example, content is stored on various servers andbefore the content can be rendered it is first fetched. In addition, adecision can be made as to which other channel(s) and/or information(e.g., metadata) are to be pre-fetched for possible future viewing(e.g., based upon permissions and /or other metrics). In one example,information can be pre-fetched for the last x number (e.g., 10) channelsthat a viewer has been watching. In another example, a determination ofwhich information to pre-fetch can be based upon seasonal metrics (e.g.,by specific data). In another example, a determination of whichinformation to pre-fetch can be based upon favorites and/or userpreferences.

As described herein, various embodiments can provide formachine-learning (ML) components and/or features. In one example, ML canfacilitate ranking recommendations of which channels to go to next. Inanother example, ML can facilitate making predictions of which channelsa user will want to go to next. The recommendations and/or predictionscan be based upon, for example: (a) preferences such as: personal(viewership/genre, history, viewed parts of the series), group/aggregate(trending topics, location specialization, time of day/weekseasonality), enticing metadata attributes (faces, keywords/topics); (b)identity such as: social (current co-located company, family, etc.); (c)personalization such as: contextual (novelty or uniqueness of assetwhere a user is binging or looking for something new), mood of content,engagement of user such as: changing channels a lot or just backgroundnoise; (d) resources such as: device (capacity left on device, bandwidthavailable now, battery left), location (expected time in this location).

Reference will now be made to a discussion of channel selectionintelligence according to one or more embodiments. In one example,rendering and pre-fetching can be provided for near instant switching.In one example, mechanisms are provided to determine what channelsshould be pre-fetched and/or stored ahead of time.

In one example, predicting channels/artificial intelligence (AI) canallow pre-fetching at different levels (e.g., higher likelihood ofchannel discovery). In one specific example, AI can distinguishlikelihood of future viewer desire with respect to pre-fetching (e.g.,likelihood tiers).

In one example, pre-fetching can have different stages. In one specificexample, the stages can comprise: (a) pre-fetch metadata—e.g., 100s ofdata (Live and/or VOD); pre-fetch manifest (in one example, thisconstantly changes (e.g., every 6 seconds); (c) pre-fetch DRM resources(e.g., more restrictive elements and allocation).

In one example, user interaction can be in the context of live channelpre-fetch of content (e.g., the ten last channels in this state will bepreserved).

In one example, there can be different levels of pre-fetch. In onespecific example, in a case in which there are a significant amount ofsuggestions, the pre-fetched information can be more static (e.g.,initialization data). In another specific example, the pre-fetchingmechanism can distinguish between on-demand and live content.

In one example, the AI can utilize a model that learns from itself(e.g., by monitoring previous channels). In one example, a continuouslearning can be utilized. In one example, a reinforcement learning canbe utilized (e.g., where reward is payoff on accuracy).

In one example, the AI can monitor immediate social information of theuser(s). In one example, two sets of AI can be used. In one specificexample, one AI is local (for the device and location characteristics)and one AI is remote (for preference and viewership).

In one example, divergent pre-fetch recommendations can be provided. Inone specific example, such recommendations can be based upon behavior ofthe user's consumption and channel changing (via which you can determinethat they want to find something that is totally different). In onespecific example, one behavior can be using local cache (no hits in the10 that were previously cached) and another behavior can be seeing thata viewer is watching things that are atypical for the user for that timeof day (e.g., you usually watch news at this time, but now you'rewatching action movies). In another example, the AI can look at longtail of exposure or viewership (has the user ever been exposed to thiscontent? is it related?).

Referring now to determining co-presence of the location according tovarious embodiments, some methods and solutions are as follows: (a)determine co-presence by cues from the application—are there multipleusers logged into the account, are there multiple data streams going tothis application; (b) other cues could be from other viewershipanalysis—seeing that a viewer had watched a certain channel to determinesimilar concepts; (c) yet other cues could be notifications that comeback to the user e.g., “I see that you're watching a new video” or “Isee that your video is related to the group watch X” and then “do youwant to switch?”—if this switch is executed then it can restart thecaching process.

Reference will now be made to a discussion of various model formulationsaccording to various embodiments. One example of these formulationsrelates to recommendation/profile learning as follows: the input ishistorical viewership or affinity towards a program; features can bederived directly from that program's descriptive metadata from anelectronic programming guide (EPG) (e.g. keywords, actors, mood, genre,etc.); the model can be traditional learning platform (DNN, SVM,clustering, etc.); additionally, can compute seasonality ofrecommendation as regression model (e.g., viewer watches program X atthis time of day); the output is recommendation of that program beinginteresting (such output can be used in the various pre-fetchingmethodologies described herein).

Another example of the above-mentioned formulations relates to identityand social determination as follows: the input is from physicalindicator (e.g., co-located phones from peer-level BLUETOOTH®, detectedface from forward camera, detected other device on network (e.g. in-homeLAN); features are co-occurrence of physical indicator and content; themodel can be traditional learning method (DNN, SVM, clustering) and/orthose with social graph analysis (for co-presence and affinity to acommon feature); the output is recommendation of content (such outputcan be used in the various pre-fetching methodologies described herein).

Another example of the above-mentioned formulations relates topersonalization as follows: the input is behavioral viewing orapplication usage—has the user changed channels a lot or are theywatching a program that is different from what they normally watch atthis time; features are what the channel change rate is, how differentis the content from what is typical, other seasonal inputs, time of day,etc.; model method can be GAN-based (a Generative Adversarial Network,because it is adversarially trying to recommend things that are out ofcontext and yet very specific) but still providing a classification ofrelevant or not; output is recommendation of content (such output can beused in the various pre-fetching methodologies described herein).

Another example of the above-mentioned formulations relates to resourceestimation as follows: the input is device and local information that isspecific to consumption platform; features are resource availability,time in location, bandwidth capability, etc.; model is traditionalmodel; output recommendation either for a specific program or a specifictype of program (e.g. duration of program, bit-rate complexity of theprogram/channel). Such output can be used in the various pre-fetchingmethodologies described herein.

Another example of the above-mentioned formulations relates to overallmodel architecture as follows: DNNs are performant for most models andin one embodiment, local features are run with local model and thenothers are run at network/edge and pooled locally.

Another example of the above-mentioned formulations relates to overallranking model as follows: DNN regression can be used for this type ofmodel that will take the outputs of other recommendations and pool/rankthem for optimal ranking / recommendation.

In another example, if it is determined that a viewer is watching with agroup, then a switch could be made to a profile associated with thatgroup.

In various embodiments, any desired process (or processes) can beperformed using artificial intelligence, machine learning or anycombination thereof

In various embodiments, any desired process (or processes) can beperformed dynamically, programmatically or any combination thereof.

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular, avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of communicationnetwork 100, the subsystems and functions of system 200, and methods2000, 2100, 2200 presented in FIGS. 1, 2A, 2B, 2C, 2D and 3. Forexample, virtualized communication network 300 can facilitate in wholeor in part determining (or predicting): (a) for which channels topre-fetch information; and/or (b) when the information should bepre-fetched for one or more channels. Further, virtualized communicationnetwork 300 can facilitate in whole or in part pre-fetching of suchinformation.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part determining (or predicting): (a) forwhich channels to pre-fetch information; and/or (b) when the informationshould be pre-fetched for one or more channels. Further, computingenvironment 400 can facilitate in whole or in part pre-fetching of suchinformation.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part determining (or predicting): (a) for which channelsto pre-fetch information; and/or (b) when the information should bepre-fetched for one or more channels. Further, platform 510 canfacilitate in whole or in part pre-fetching of such information. In oneor more embodiments, the mobile network platform 510 can generate andreceive signals transmitted and received by base stations or accesspoints such as base station or access point 122. Generally, mobilenetwork platform 510 can comprise components, e.g., nodes, gateways,interfaces, servers, or disparate platforms, that facilitate bothpacket-switched (PS) (e.g., internet protocol (IP), frame relay,asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic(e.g., voice and data), as well as control generation for networkedwireless telecommunication. As a non-limiting example, mobile networkplatform 510 can be included in telecommunications carrier networks, andcan be considered carrier-side components as discussed elsewhere herein.Mobile network platform 510 comprises CS gateway node(s) 512 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 540 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology(ies) utilizedby mobile network platform 510 for telecommunication over a radio accessnetwork 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part determining (orpredicting): (a) for which channels to pre-fetch information; and/or (b)when the information should be pre-fetched for one or more channels.Further, computing device 600 can facilitate in whole or in partpre-fetching of such information. The communication device 600 cancomprise a wireline and/or wireless transceiver 602 (herein transceiver602), a user interface (UI) 604, a power supply 614, a location receiver616, a motion sensor 618, an orientation sensor 620, and a controller606 for managing operations thereof. The transceiver 602 can supportshort-range or long-range wireless access technologies such asBLUETOOTH®, ZigBee°, Wi-Fi, DECT, or cellular communicationtechnologies, just to mention a few (BLUETOOTH® and ZigBee® aretrademarks registered by the BLUETOOTH® Special Interest Group and theZigBee® Alliance, respectively). Cellular technologies can include, forexample, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR,LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example BLUETOOTH®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, Wi-Fi, BLUETOOTH®, or otherwireless access points by sensing techniques such as utilizing areceived signal strength indicator (RSSI) and/or signal time of arrival(TOA) or time of flight (TOF) measurements. The controller 606 canutilize computing technologies such as a microprocessor, a digitalsignal processor (DSP), programmable gate arrays, application specificintegrated circuits, and/or a video processor with associated storagememory such as Flash, ROM, RAM, SRAM, DRAM or other storage technologiesfor executing computer instructions, controlling, and processing datasupplied by the aforementioned components of the communication device600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying channels for which information is to bepre-fetched) can employ various AI-based schemes for carrying outvarious embodiments thereof. Moreover, a classifier can be employed todetermine a ranking or priority associated with the pre-fetching. Aclassifier is a function that maps an input attribute vector, x=(x1, x2,x3, x4, xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naive Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: selecting from among a pluralityof channels a subset of channels for which to pre-fetch data, theselecting being according to predictions that each channel of the subsetof channels is more likely to be requested by a user who is viewing afirst video than each channel of the plurality of channels that is notpart of the subset; prioritizing the subset of channels such that afirst channel of the subset of channels has a priority over a secondchannel of the subset of channels, the first channel being given thepriority based upon a prediction that the first channel is more likelyto be requested by the user than the second channel; pre-fetching, forthe first channel, first data of a first type and second data of asecond type, the first data of the first type being manifest data; andpre-fetching, for the second channel, third data of the first typewithout pre-fetching any data of the second type.
 2. The device of claim1, wherein the operations further comprise: receiving, for a selectedchannel, the first video; receiving an instruction from the user tochange from the selected channel to the first channel; and responsive tothe instruction: receiving for the first channel a second video; andprocessing the second video for rendering on a display instead of thefirst video, the processing of the second video utilizing the first dataof the first type and the second data of the second type.
 3. The deviceof claim 1, wherein the operations further comprise: receiving, for aselected channel, the first video; receiving an instruction from theuser to change from the selected channel to the second channel; andresponsive to the instruction: receiving for the second channel a secondvideo; and processing the second video for rendering on a displayinstead of the first video, the processing of the second video utilizingthe third data of the first type without utilizing any data of thesecond type.
 4. The device of claim 1, wherein the selecting, theprioritizing, or any combination thereof is performed using artificialintelligence, machine learning, or any combination thereof.
 5. Thedevice of claim 1, wherein: the second data of the second type is DRM(digital rights management) data.
 6. The device of claim 1, wherein: themanifest data is used in conjunction with initialization data.
 7. Thedevice of claim 1, wherein the operations further comprise pre-fetchingfirst video data corresponding to the first channel, pre-fetching secondvideo data corresponding to the second channel, or any combinationthereof.
 8. The device of claim 1, wherein: the operations furthercomprise processing the first video for rendering on a display beingviewed by the user; the processing of the first video for rendering onthe display is performed by a first portion of the processing system;the pre-fetching, for the first channel, is performed by a secondportion of the processing system, the second portion being distinct fromthe first portion; the first portion comprising one of first hardware,first firmware, first software, or any combination thereof; and thesecond portion comprising one of second hardware, second firmware,second software, or any combination thereof.
 9. The device of claim 8,wherein: the pre-fetching, for the second channel, is performed by athird portion of the processing system, the third portion being distinctfrom the first portion and being distinct from the second portion; andthe third portion comprising one of third hardware, third firmware,third software, or any combination thereof.
 10. A non-transitorymachine-readable medium comprising executable instructions that, whenexecuted by a processing system including a processor, facilitateperformance of operations, the operations comprising: identifying fromamong a plurality of channels a subset of channels for which topre-fetch data, the identifying being according to predictions that eachchannel of the subset of channels is more likely to be requested by auser who is viewing a first video, during a subsequent channel switchingprocess, than each channel of the plurality of channels that is not partof the subset; assigning a respective priority to each channel of thesubset of channels such that a first channel of the subset of channelshas a first priority that is above a second priority of a second channelof the subset of channels, the first channel being assigned the firstpriority and the second channel being assigned the second priority basedupon a prediction that the first channel is more likely to be requestedby the user during the subsequent channel switching process than thesecond channel; pre-fetching, for the first channel, first data of afirst type and second data of a second type, the first data of the firsttype being manifest data; and pre-fetching, for the second channel,third data of the first type without pre-fetching any data of the secondtype.
 11. The non-transitory machine-readable medium of claim 10,wherein: the switching to the first channel comprises processing asecond video for rendering on a display; and the switching to the secondchannel comprises processing a third video for rendering on the display.12. The non-transitory machine-readable medium of claim 11, wherein: thefirst data includes initialization data.
 13. The non-transitorymachine-readable medium of claim 10, wherein: the executableinstructions comprise a first set of executable instructions and asecond set of executable instructions; the first set of executableinstructions is distinct from the second set of executable instructions;the identifying and the assigning are performed by the first set ofexecutable instructions; and the pre-fetching, for the first channel, isperformed by the second set of executable instructions.
 14. Thenon-transitory machine-readable medium of claim 13, wherein: theexecutable instructions further comprise a third set of executableinstructions; the third set of executable instructions is distinct fromthe first set of executable instructions; the third set of executableinstructions is distinct from the second set of executable instructions;and the pre-fetching, for the second channel, is performed by the thirdset of executable instructions.
 15. A method, comprising: selecting, bya processing system including a processor, from among a plurality ofchannels a subset of channels for which to pre-fetch data, the selectingbeing according to predictions that each channel of the subset ofchannels is more likely to subsequently be selected by a viewer thaneach channel of the plurality of channels that is not part of thesubset; assigning, by the processing system, a respective priority toeach channel of the subset of channels such that a first channel of thesubset of channels has a first priority that is higher than a secondpriority of a second channel of the subset of channels, the firstchannel being assigned the first priority and the second channel beingassigned the second priority based upon a prediction that the firstchannel is more likely to subsequently be selected by the viewer duringa subsequent channel switching process than the second channel;pre-fetching by the processing system, for the first channel, first dataof a first type and second data of a second type, the first data of thefirst type being manifest data; and pre-fetching by the processingsystem, for the second channel, third data of the first type withoutpre-fetching any data of the second type.
 16. The method of claim 15,wherein the second data of the second type is DRM (digital rightsmanagement) data.
 17. The method of claim 15, wherein: the selecting,the assigning, or any combination thereof is performed dynamically,programmatically, or any combination thereof.
 18. The method of claim15, further comprising: performing, by the processing system, thesubsequent channel switching process; wherein the subsequent channelswitching process comprises switching to the first channel; and whereinthe performing the subsequent channel switching process utilizes thefirst data of the first type and the second data of the second type. 19.The method of claim 15, further comprising: performing, by theprocessing system, the subsequent channel switching process; wherein thesubsequent channel switching process comprises switching to the secondchannel; and wherein the performing the subsequent channel switchingprocess utilizes the third data of the first type without utilizing anydata of the second type.
 20. The method of claim 15, wherein themanifest data is used in conjunction with initialization data.